Coupling of Co-expression Network Analysis and Machine Learning Validation Unearthed Potential Key Genes Involved in Rheumatoid Arthritis - Xiao, Jianwei; Wang, Rongsheng; Cai, Xu; Ye, Zhizhong (Frontiers in Genetics, 2021)
Rheumatoid arthritis (RA) is an incurable disease that afflicts 0.5-1.0% of global population though it is less threatening at its early stage. Therefore, improved diagnostic efficiency and prognostic outcome are critical for confronting RA. Although machine learning is considered a promising technique in clinical research, its potential in verifying the biological significance of gene was not fully exploited. The performance of a machine learning model depends greatly on the features used for model training; therefore, the effectiveness of prediction might reflect the quality of input features. In the present study, we used WGCNA in conjunction with DEG analysis to select key genes that were highly associated with RA phenotypes based on multiple microarray datasets of RA blood samples, after which they were used as features in machine learning model validation. A total of 6 machine learning models were used to validate the biological significance of the key genes based on gene expression, among which 5 models achieved good performances (AUC > 0.85), suggesting that our currently identified key genes are biologically significant and highly representative of genes involved RA. Combined with other biological interpretations including GO analysis, protein-protein (PPI) network analysis as well as inference of immune cell composition, our current study might shed a light on in-depth study of RA diagnosis and prognosis.